In-service Restoration (SR), the healthy section of the feeder can be reenergized by finding the optimal path for power flow. Through conventional methods which are mainly deterministic in nature, the computational burden is very high. Therefore, researchers have proposed various meta-heuristic based methods to solve the SR problem. But since, these methods are probability based; one single algorithm cannot guarantee optimal solution for all scenarios. Hence, the authors have proposed a Machine Learning (ML) based framework, which can predict the best SR scheme for a particular fault scenario among the SR solutions obtained through various meta-heuristic algorithms. The supervised ML model is developed using the fault features as input values and the best performing meta-heuristic algorithm as the target value. To check the validity of the ML framework, the authors have taken four different meta-heuristic algorithms, which are, Enhanced Integer Coded Particle Swarm Optimization (EICPSO), Shuffled Frog Leaping Algorithm (SFLA), Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Ant Colony Optimization (ACO) algorithm. The ML model can be extended for

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Diabetic retinopathy (DR) is one of the major complications caused by diabetes and is usually identified from retinal fundus images. Screening of DR from digital fundus images could be time-consuming and error-prone for ophthalmologists. For efficient DR screening, good quality of the fundus image is essential and thereby reduces diagnostic errors. Hence, in this work, an automated method for quality estimation (QE) of digital fundus images using an ensemble of recent state-of-the-art EfficientNetV2 deep neural network models is proposed. The ensemble method was cross-validated and tested on one of the largest openly available datasets, the Deep Diabetic Retinopathy Image Dataset (DeepDRiD). We obtained a test accuracy of 75% for the QE, outperforming the existing methods on the DeepDRiD. Hence, the proposed ensemble method may be a potential tool for automated QE of fundus images and could be handy to ophthalmologists.

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